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Journal Abstract Search

159 related items for PubMed ID: 34666801

  • 1.
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  • 2. High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity.
    Meacham-Hensold K, Montes CM, Wu J, Guan K, Fu P, Ainsworth EA, Pederson T, Moore CE, Brown KL, Raines C, Bernacchi CJ.
    Remote Sens Environ; 2019 Sep 15; 231():111176. PubMed ID: 31534277
    [Abstract] [Full Text] [Related]

  • 3. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information.
    Song G, Wang Q, Jin J.
    J Plant Physiol; 2022 Dec 15; 279():153831. PubMed ID: 36252398
    [Abstract] [Full Text] [Related]

  • 4. Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression.
    Fu P, Meacham-Hensold K, Guan K, Wu J, Bernacchi C.
    Plant Cell Environ; 2020 May 15; 43(5):1241-1258. PubMed ID: 31922609
    [Abstract] [Full Text] [Related]

  • 5. Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms.
    Fu P, Meacham-Hensold K, Guan K, Bernacchi CJ.
    Front Plant Sci; 2019 May 15; 10():730. PubMed ID: 31214235
    [Abstract] [Full Text] [Related]

  • 6. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression.
    Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP.
    J Exp Bot; 2021 Sep 30; 72(18):6175-6189. PubMed ID: 34131723
    [Abstract] [Full Text] [Related]

  • 7. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.
    Montes CM, Fox C, Sanz-Sáez Á, Serbin SP, Kumagai E, Krause MD, Xavier A, Specht JE, Beavis WD, Bernacchi CJ, Diers BW, Ainsworth EA.
    Genetics; 2022 May 31; 221(2):. PubMed ID: 35451475
    [Abstract] [Full Text] [Related]

  • 8. Non-invasive diagnosis of wheat stripe rust progression using hyperspectral reflectance.
    Cross JF, Cobo N, Drewry DT.
    Front Plant Sci; 2024 May 31; 15():1429879. PubMed ID: 39323538
    [Abstract] [Full Text] [Related]

  • 9. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy.
    Wang S, Guan K, Wang Z, Ainsworth EA, Zheng T, Townsend PA, Li K, Moller C, Wu G, Jiang C.
    J Exp Bot; 2021 Feb 02; 72(2):341-354. PubMed ID: 32937655
    [Abstract] [Full Text] [Related]

  • 10. Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance.
    Sexton T, Sankaran S, Cousins AB.
    J Exp Bot; 2021 May 28; 72(12):4373-4383. PubMed ID: 33735372
    [Abstract] [Full Text] [Related]

  • 11. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.
    Zhang J, Zhang W, Xiong S, Song Z, Tian W, Shi L, Ma X.
    Plant Methods; 2021 Mar 31; 17(1):34. PubMed ID: 33789711
    [Abstract] [Full Text] [Related]

  • 12. High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.
    Yendrek CR, Tomaz T, Montes CM, Cao Y, Morse AM, Brown PJ, McIntyre LM, Leakey AD, Ainsworth EA.
    Plant Physiol; 2017 Jan 31; 173(1):614-626. PubMed ID: 28049858
    [Abstract] [Full Text] [Related]

  • 13. Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat.
    Silva-Perez V, Molero G, Serbin SP, Condon AG, Reynolds MP, Furbank RT, Evans JR.
    J Exp Bot; 2018 Jan 23; 69(3):483-496. PubMed ID: 29309611
    [Abstract] [Full Text] [Related]

  • 14. Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat grown in simulated saline field conditions.
    El-Hendawy S, Al-Suhaibani N, Elsayed S, Alotaibi M, Hassan W, Schmidhalter U.
    Plant Physiol Biochem; 2019 Nov 23; 144():300-311. PubMed ID: 31605962
    [Abstract] [Full Text] [Related]

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  • 16. Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.
    El-Hendawy S, Junaid MB, Al-Suhaibani N, Al-Ashkar I, Al-Doss A.
    Plants (Basel); 2024 Sep 19; 13(18):. PubMed ID: 39339585
    [Abstract] [Full Text] [Related]

  • 17. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.
    Yu S, Fan J, Lu X, Wen W, Shao S, Guo X, Zhao C.
    Front Plant Sci; 2022 Sep 19; 13():927832. PubMed ID: 35845657
    [Abstract] [Full Text] [Related]

  • 18. Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
    Barnes ML, Breshears DD, Law DJ, van Leeuwen WJD, Monson RK, Fojtik AC, Barron-Gafford GA, Moore DJP.
    PLoS One; 2017 Sep 19; 12(12):e0189539. PubMed ID: 29281709
    [Abstract] [Full Text] [Related]

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  • 20. High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: a case study with a maize diversity panel.
    Ge Y, Atefi A, Zhang H, Miao C, Ramamurthy RK, Sigmon B, Yang J, Schnable JC.
    Plant Methods; 2019 Sep 19; 15():66. PubMed ID: 31391863
    [Abstract] [Full Text] [Related]

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